A survey on applications of machine learning techniques for medical image segmentation

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    With development of science and technology in this digital era, the digital imaging is increasing expeditiously in every field. This digital image processing converts the image into its digital form to perform some operations on it to get either improved version of image or to get informational features from it. Different image processing techniques are available and image segmentation has a prime role in it. Segmentation of image is done principally to separate objects of interests from backgrounds. Ample of techniques are available for image segmentation. But, sometimes many techniques fail sporadically to yield the desired outcome. To fill up the requirement, the machine learning techniques come into play and perform well with satisfactory results. Here, is a fleeting review of machine learning techniques, mainly focusing on the artificial neural network with highlight of its improvements towards deep learning and convolution neural network along with some light on other machine learning techniques. It also includes brief descriptions of some neural networks used for segmenting different medical images and focus is given on convolution neural network which is developed primarily to work with images. The review will provide researchers a visualization and ideas to further use these techniques in improved ways for better performance for image segmentation.

     

     


  • Keywords


    Image Segmentation; Machine Learning; Deep Learning; Convolution Neural Network.

  • References


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Article ID: 19005
 
DOI: 10.14419/ijet.v7i4.19005




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